DNA markers for meat tenderness

ABSTRACT

A method for assessing the tenderness of meat from an animal, comprising the step of testing the animal for the presence or absence of a genetic marker selected from the group consisting of:
         (1) an allele of the gene encoding calpastatin (CAST) associated with peak-force variation or genetic variation located other than in the CAST gene which shows allelic association with the CAST allele; and   (2) an allele of the gene encoding lysyl oxidase (LOX) associated with instron compression of the semitendinosis muscle or genetic variation located other than in the LOX gene which shows allelic association with the LOX allele.

TECHNICAL FIELD

The present invention is concerned with genetic markers for meattenderness in animals, and with methods and oligonucleotide probes forassessing meat tenderness in said animals, and a kit for this purpose.The invention is useful for the selection of animals which showdesirable traits in meat tenderness either for breeding or to selectanimals destined to be slaughtered for food.

BACKGROUND ART

Meat tenderness is an important issue for consumers, and one which caninfluence demand sufficiently for an especially tender meat to command apremium price in the marketplace. The physiological change in musclestructure during the postmortem period is complex but clearly seems tobe at least one factor in meat tenderness. The calpain/calpastatinsystem is an endogenous, calcium-dependent proteinase system, theorisedto initiate in vivo muscle protein degradation. Calpastatin appears toinhibit calpain activity and therefore may be assumed to have a role inmeat tenderness through the regulation of postmortem proteolysis. Inparticular, calpain is response for the breakdown of myofibril protein,which is closely related to meat tenderness.

It might therefore be suspected that calpastatin activity will berelated to meat tenderness. Indeed, an increase in postmortemcalpastatin activity has been correlated to reduced meat tenderness.Nevertheless, despite such observations, no clear link between the CASTgene, which encodes calpastatin, and meat tenderness has beenestablished.

For example, Lonergan et al. (1995) undertook a restriction fragmentlength polymorphism analysis at CAST and failed to find an associationwith either calpastatin activity or tenderness in cross bred offspringof sires from eight breeds. Chung et al. (1999) measured calpastatinactivity, Warner-Bratzler Shear Force and myofibril fragmentation indexin forty-seven purebred Angus bulls. However, they concluded that “PCRsingle-strand conformation polymorphism analysis of the calpastatin genewas not useful for prediction of calpastatin activity, myofibrilfragmentation index or meat tenderness”.

It is long known that one of the actions of lysyl oxidase (LOX) is toinitiate crosslink formation at an early stage in collagenfibrillogenesis (e.g., Cronlund et al., 1985). The action of lysyloxidase is intensively studied with hundreds of publications on avariety of aspects of its importance in cancer (Giampuzzi et al., 2001),the vasculature (Nellaiappan et al.) and other tissue and organ systems.

Variation at the gene itself has not been associated with differences inbeef tenderness although LOX has always been seen as a strong candidateon biochemical grounds for a gene contributing to the collagen componentof tenderness. Analysis of genetic linkage has implicated the genomicregion that includes LOX in linkage analysis of family variation inadhesion and instron compression of the semitendinosis muscle (STADH andSTIC; Drinkwater et al., 1999). However, LOX itself has not beenassociated with these measures of tenderness through the study ofpopulation associations.

Meat tenderness is a complicated trait because there are many sources ofvariation that affect postmortem meat tenderisation. Some of these arenon-genetic effects such as the age of the beast, the nature of itsfeed, degree of stress prior to slaughter, carcass chilling, postmortemageing time and cooking and testing methods. It has been suggested (e.g.Koohmaraie (1994)) that approximately 30% of the variation in tendernessin meat can be explained by additive gene effects within a single breed,and that approximately 70% of the variation is explained byenvironmental and non-additive gene effects. In the Lonergan study thecattle were slaughtered at just over 1 year of age (430 days), thesample contained only 83 animals of random peak-force values, and thesample consisted entirely of crosses between various taurine breeds.Likewise, in the Chung study purebred Angus bulls only 280 days of agewere used. In addition, in neither study were the animales selected forextreme peak-force values, and it therefore seems that environmental andnon-fixed genetic effects may have contributed to the failure toidentify any genetic linkage between the CAST gene and meat tenderness.

SUMMARY OF THE INVENTION

Through using a protocol designed to reduce the influence of fixedgenetic and environmental effects, the present inventor was unexpectedlyable to show allelic association between the CAST and LOX genes and meattenderness. In general terms, therefore, the present invention isconcerned with genetic markers for meat tenderness in animals killed formeat which are polymorphisms of the CAST and LOX genes or polymorphismswhich show allelic association therewith.

Accordingly, in a first aspect of the present invention there isprovided a method for assessing the tenderness of meat from an animal,comprising the step of testing the animal for the presence or absence ofa genetic marker selected from the group consisting of:

-   -   (1) an allele of the gene encoding calpastatin (CAST) associated        with peak-force variation or genetic variation located other        than in the CAST gene which shows allelic association with the        CAST allele; and    -   (2) an allele of the gene encoding lysyl oxidase (LOX)        associated with variation in instron compression of the        semitendinosis muscle or genetic variation located other than in        the LOX gene which shows allelic association with the LOX        allele.

Preferably, the allele tested for is located in the 3′ UTR of CAST, andis typically CAST3 D/E allele 1, having the following partial DNAsequence:

catttggaaaacgatgcctcacgtgttcttcagtgttctgatttctcat (SEQ ID NO:1)gacccctttcctcttGgacttgtgggactgtgtttgatgtttccctgggttgttgtttataagtcagtcataaAatactgtgcattgggcacatgtctcctcttgagctgctaatc gtaga,

CAST3 D/E allele 2, having the following partial DNA sequence:

catttggaaaacgatgcctcacgtgttcttcagtgttctgatttctcat (SEQ ID NO:2)gacccctttcctcttAgacttgtgggactgtgtttgatgtttccctgggttgttgtttataagtcagtcataaAatactgtgcattgggcacatgtctcctcttgagctgctaatc gtaga

or CAST3 D/E allele 3, having the following partial DNA sequence:

catttggaaaacgatgcctcacgtgttcttcagtgttctgatttctcat (SEQ ID NO:3)gacccctttcctcttAgacttgtgggactgtgtttgatgtttccctgggttgttgtttataagtcagtcataaTatactgtgcattgggcacatgtctcctcttgagctgctaatc gtaga.

Reduced meat toughness is selected for by rejecting animals with the“11” and “12” genotypes and accepting animals with the “22” or “23”genotypes. In the sequences given above, the allelic difference ishighlighted with a capital letter. These three alleles in the D/E DNAfragment are due to two SNP (single nucleotide polymorphisms). The firstSNP is at base 2655 of Genbank sequence L14450, which is the same asbase 2959 of Genbank sequence AF159246; it is a G to A change so thatallele 1 has G and alleles 2 and 3 have A. The second SNP is an A to Tchange 58 base pairs 3′ to the first SNP. Since only three alleles havebeen noted for this region, with 2 SNPS, it implies that there iscomplete linkage disequillibrium between allele 2 and allele 3, andallele 3 may have evolved from allele 2. This is expected since they are58 base pairs apart. For predictive purposes, a test of the second SNPwhich gives a result of allele 3 is equivalent to a test of the firstSNP giving a result of allele 2. This is consistent with the peak forcevalues of animals that are ‘23’ heterozygotes, all of whom have low peakforce values. While not wishing to be bound by theory, it is believedthat these polymorphisms are linked to a mutation in or near thecalpastatin gene (perhaps in the promoter or an intron) which results inreduced calpastatin expression or activity.

A further polymorphism has been identified in the 5′ UTR of the CASTgene and other polymorphisms which exhibit allelic association with thepolymorphism of the 3′ UTR, and therefore also act as genetic markersfor the tenderness characteristics described above, may also be presentat least within the genomic DNA embracing the coding region of the CASTgene and the 5′ UTR and 3′ UTR regions of that gene. In addition, wherethere has been a recent reduction in population size for a species,particular haplotypes of individuals will be relativelyover-represented. If insufficient time has elapsed to cause allelicassociation to decay, there will be linkage disequilibrium even foralleles which are far apart. Livestock species such as cattle have beendomesticated from a relatively small pool of wild ancestors in recenttimes, and therefore in these species allelic association is foundbetween alleles that may be remote physically. Thus, it may be expectedthat regions of genetic variation that are outside the CAST gene willalso show allelic association with the polymorphisms in the CAST genedescribed above, and therefore will be suitable genetic markers for thecharacteristic of peak-force variation. Hence, these polymorphisms mayalso be used to assess meat tenderness.

In particular the CAST5 microsatellite polymorphism (Nonneman et al,1999) has been found to be useful as a genetic marker for meattenderness. Of the more common alleles, alleles 7 and 9 have been foundto be associated with low peak-force ad allele 3 to be associated withhigh peak-force.

Therefore, the invention encompasses, in preferred embodiments, thefurther step of testing for the presence or absence of one or moreadditional genetic markers such as alleles of the gene encodingcalpastatin associated with peak-force variation, in particular, withtesting for the presence or absence of CAST5 allele 7 or 9 and/or thepresence or absence of CAST5 allele 3. The most favorable results whenthe presence of CAST D/E allele 2 has been established is to have CAST5allele 7 or allele 9 present also, whereas the benefits of the presenceof CAST D/E allele 2 are to some degree counteracted if the animal alsopossesses CAST5 allele 3.

The LOX polymorphism has also been shown to be a genetic marker for meattenderness, and allele 1 or allele 2 may be tested for. Just as for theCAST gene, allelic association may be exhibited to alleles locatedoutside the LOX gene.

The LOX polymorphism has the following partial sequence in which the A-Tvariant causes the SSCP with allele 1 equal to base T and allele 2 equalto base A:

TTATCACTGATGTCAAACCTGGAAACTATATTCTCAAGGTAGAGAACTTT (SEQ ID NO:4)GAACATATACCCATAATGTATTTCAATTGTGACTCAGTGGGCTTATTCTCTGGAGTCAAATGTTAAATATTCATGGTCCTGCAAACAATTATACATCTTCTAGAACTACTT(C/T)TAAACCAACCTAGATATATT(A/T)AAAAAATTCTTATTTGAAAGAACTTTATGGAAAAAGATCCAGCCTCCTTCAAAAACTCCAGAGTTGAAACACATGCCTAACTTACACCCTCTTCCTTGCCTGATTTAGTTGAATTATGCTGTCTCTATTTTAGCCTCCATTCTGGAAAGAGGAAAAAAATTAACCAGTAAACACTGCTGATGAAATCTGAAACACAGATGATGTTTGTTTTGCCTAGGTCAGTGTGAATCCCAGCTATTTGGTGCC.

According to a second aspect of the present invention, there is provideda genetic marker for meat tenderness in an animal which is a polymorphicform of the CAST gene, being the CAST3 D/E polymorphism or the LOXpolymorphism.

According to a third aspect of the present invention there is providedan isolated DNA molecule comprising the nucleotide sequence set forth inSEQ ID NO:1, SEQ ID NO:2 or SEQ ID NO:3.

According to a fourth aspect of the present invention there is providedan isolated DNA molecule consisting of the nucleotide sequence set forthin SEQ ID NO:1, SEQ ID NO:2 or SEQ ID NO:3.

According to a fifth aspect of the present invention there is provided amethod for selecting an animal likely to yield meat of improvedtenderness, comprising the steps of:

-   -   (1) testing the animal for the presence of an allele of the gene        encoding calpastatin (CAST) associated with low peak-force or        genetic variation located other than in the CAST gene which        shows allelic association with the CAST allele and/or for the        presence of an allele of the LOX gene associated with the low        instron compression of the semitendinosis muscle or genetic        variation located other than in the LOX gene which shows allelic        association with the LOX allele; and    -   (2) selecting animals which have the CAST and/or LOX allele        and/or genetic variation in allelic association therewith.

Advantageously, in order to assess the tenderness of meat from an animaland/or select an animal likely to yield meat of improved tendernesstesting may comprise the steps of:

-   -   (1) obtaining a biological sample from the animal;    -   (2) extracting DNA from the sample;    -   (3) amplifying DNA from the CAST or LOX gene and/or from regions        of genetic variation which show allelic association to        polymorphisms of the relevant one of the CAST or LOX gene; and    -   (4) identifying the allele present in the amplified DNA.

Typically the allele identified in step (4) is one of CAST3 D/E allele1, CAST3 D/E allele 2 and CAST3 D/E allele 3 described above and/or theCAST5 alleles described above.

Preferably the biological sample is blood, but other biological samplesfrom which DNA can be amplified may be used. For example, hair rootsamples, cheek scrapings, skin samples and the like may be used.

Typically amplification is performed using the polymerase chain reaction(PCR), but other DNA amplification methods such as the ligase chainreaction are well known in the art, and may alternatively be used.

Preferably the alleles are identified by polyacrylamide gelelectrophoresis techniques such as SSCP, or by other techniques wellknown to the person skilled in the art such as RFLP analysis.

In a sixth aspect the invention provides an oligonucleotide probe foramplification of a genetic marker associated with peak-force variation,said genetic marker being either an allele of the gene encodingcalpastatin (CAST) or genetic variation located other than in the CASTgene which shows allelic association with said allele.

Typically the probe is selected from the group consisting of:

castd 5′ cat ttg gaa aac gat gcc tca c 3′ (SEQ ID NO:5) caste 5′ tct acgatt agc agc tca aga gga g 3′ (SEQ ID NO:6) CAST5U15′-GTAAAGCCGCACAAAACACACCCAGG-3′ (SEQ ID NO:7) CAST5D15′-GTTTCTGGACCCTCTGGATGAGGAAGCGG-3′. (SEQ ID NO:8)

In view of the designation of the primers as CASTD and CASTE, theamplified fragment of the CAST gene is referred to from time to time asthe CAST D/E fragment and the polymorphism as the CAST D/E polymorphism.

According to a seventh aspect of the present invention there is providedan oligonucleotide probe for amplification of a genetic markerassociated with variation in instron compression of the semitendinosismuscle, the genetic marker being either an allele of the gene encodinglysyl oxidase (LOX) or genetic variation located other than in the LOXgene which shows allelic association with said allele.

Typically the oligonucleotide probe is an oligonucleotide probe selectedfrom the group consisting of:

LOX K5: 5′ tat cac tga tgt caa acc tg 3′ (SEQ ID NO:9) LOX K6: 5′ actcag gca cca aat agc tg 3′. (SEQ ID NO:10)

According to an eighth aspect of the present invention there is provideda kit for use in assessing the tenderness of meat from an animal and/orselecting an animal likely to yield meat of improved tenderness,comprising oligonucleotide probes for amplification of at least onegenetic marker for meat tenderness, said genetic marker being either anallele of the gene encoding calpastatin (CAST) or genetic variationlocated other than in the CAST gene which shows allelic association withsaid allele, or an allele of the LOX gene associated with low instroncompression of the semitendinosis muscle or genetic variation locatedother than in the LOX gene which shows allelic association with the LOXallele, and means for amplifying DNA.

The primers used to amplify the DNA are the CASTD and CASTE primersand/or the CAST5U1 and CAST5D1 primers for amplifying the CAST5polymorphism. However, other primers able to amplify polymorphismsassociated with a reduction in toughness in meat are envisaged, whetherthese be primers which amplify a polymorphism other than the CAST3 D/Epolymorphism or CAST5 polymorphism, or other primers able to amplify theCAST3 D/E fragment of CAST5 polymorphism.

The methods of the invention may be used both for the selection ofbreeding animals and for the selection of unpedigreed animals for entryinto feed lots. In the latter case, the methods of the invention allowfor animals with unsuitable pedigrees to be excluded from feed lots onthe basis that highly tender meat is unlikely to be attained with theseanimals even after a long feed lot holdings. Alternatively, suchmeasurements may allow for determination of the optimum time to reachmaximum meat tenderness. The invention is therefore also concerned withanimals when selected by the method of the invention, their progeny andthe use of both selected animals and their progeny for breeding, as wellas meat from these animals.

The methods of the invention are applicable to animals including but notlimited to cattle and other bovids, including water buffalo and bison,to other ungulates, including sheep, goats and deer, and pigs andchickens.

Throughout this specification, the words “comprise”, “comprises” and“comprising” are used in a non-exclusive sense, except where the contextrequires otherwise.

It will be clearly understood that, although a number of prior artpublications are referred to herein, this reference does not constitutean admission that any of these documents forms part of the commongeneral knowledge in the art, in Australia or in any other country.

BRIEF DESCRIPTION OF THE DRAWINGS

Preferred embodiments of the present invention will now be described, byway of example only, with reference to the accompanying drawings, inwhich:

FIG. 1 is a photograph of a single strand conformational polymorphism(SSCP) gel which shows genotypes for the CAST3 D/E polymorphism, fromleft to right, 11, 22, blank, 11, 12, 12, 12, 12, 11, 22.

FIGS. 2 a & b show the distribution of Warner-Bratzler peak-forcemeasurements in the two samples of 169 and 77 animals respectively. Notethat extremes were used so there is no middle to the distribution. Itdoes not imply that the distribution is bi-modal. Note different scalesin the figures.

FIGS. 3 a & b are a plot of the raw Warner-Bratzler peak-forcemeasurements against the CAST genotypes. Note the gap in the middle dueto the use of extremes of the distribution. Note the similarity betweenthe distributions in the two samples.

FIG. 4 is a boxplot of the residual Warner-Bratzler peak-forcemeasurements (x1) for each genotype for the first sample. The medianquarter and three-quarter percentiles, whiskers and outliers are shown.

FIG. 5 shows the distribution of DNA fragment sizes for the CAST5microsatellite. Horizontal axis is the frequency of each allele and thevertical axis is the DNA fragment size. Alleles are labelled inincreasing DNA fragment size so allele m1 in this distribution is lessthan 132 bp, m2<136 bp, m3<138 bp, m4<142 bp, m5<144 bp, m6<146 bp,m7<148 bp, m8<151 bp, m9<153 bp, m10<155 bp, m11<157 bp, m12<159 bp,m13<161 bp. DNA fragments were not found in some of the 2 bp bins, e.g.,in the less than 134 bp bin, and these are either extremely rare ornon-existent.

FIG. 6 is a box plot of raw LD peak-force values along the horizontalaxis versus CAST5 microsatelite allele identity along the vertical axis.The boxes contain the median value, represented by the dot, a boxrepresenting the 25 and 75 percentile and whiskers indicating theexpected range for the distributions, with outliers indicated by opencircles. Care must be taken in interpreting this figure since there aresome alleles that are rare, such as m1, m2 and m13 (see FIG. 5 for thefull distribution, so interpretations made on those alleles are notparticularly informative. Note particularly that this is a distributionof extreme values, so the median value will swing from a low to a highvalue if half the samples are high values.

FIG. 7 is a photograph of a single strand conformational polymorphismgel showing the genotypes of LOX from left to right, 11, 11, 12, 12, 22,11.

FIG. 8 shows the distribution of instron compression measurements forthe two samples of 166 and 87 animals combined. Note that extremes wereused so there is no middle to the distribution. It does not imply thatthe distribution is bi-modal.

FIG. 9 shows the distribution of adhesion measurements for the twosamples of 166 and 87 animals combined. Although the sample was selectedfor extremes of instron compression, this has not been translated into aseries of extreme adhesion measurements.

FIG. 10 is a plot of STIC versus STADH for the combined sample. Note thenon-uniformity caused by the selected STIC values.

MODES FOR PERFORMING THE INVENTION EXAMPLE 1 Identification of CAST3 D/EPolymorphism

Cattle were chosen from the DNA Bank of the Cattle and Beef CooperativeResearch Centre located in Brisbane, Australia to have as diverse agenetic and phenotypic background as possible. Information stored in theCRC Database was used to select animals. Animals of extremes ofpeak-force were selected, although animals with peak-force measuresabove 12 were excluded since they might have confounded peak-forcemeasurements. In essence, the procedure was to select cattle in eachcontemporary group which were of phenotypic extreme measures, to ensurethat no sire was represented by a cluster of offspring, that all marketsand finishing regimes were included in each extreme, so that extremeswere not biased by being representative of a particular market orfinishing regime. A total of 169 samples were obtained (Table 1) for thefirst sample. A second sample of 77 animals (Table 6) were analysed as acheck to determine whether the same allelic association could beobserved in another sample.

These DNA samples were genotyped for the CAST (calpastatin) D/E DNAfragment using the primers

castd 5′ cat ttg gaa aac gat gcc tca c 3′ (SEQ ID NO:5) caste 5′ tct acgatt agc agc tca aga gga g 3′. (SEQ ID NO:6)

The conditions of the polymerase chain reaction (PCR) are an annealingtemperature of 60 Celsius, 2.5 mM Magnesium chloride, and reagent mixesobtained from Biotech International. The DNA fragments were labelled viathe incorporation of ³²P dCTP into the fragments during the PCR, and thefragments were visualised by autoradiography using X-Ray film exposedovernight at room temperature. Alleles were scored in numerical orderwhere the fastest migrating allele is number 1.

The genotypes were analysed using generalised linear models (GLM)following the equation peak-force=1+genotypes nested within fixedeffects+error implemented via the S-PLUS software. Fixed effects thatwere considered were breed, finish (Australia, Korea, Japan),contemporary group (cohort), region (pasture v grain, north v south) andthe covariate of final weight. The genotypes were nested within regionand breed since pure-bred offspring of taurine sires were not pasturedin the north. The size of the effects associated with genotype wasestimated by the comparison of variances (eg, Andersson-Eklund andRendel, 1993). To estimate the size of effect associated with genotypicsubstitution, the same model was fitted without the calpastatingenotypes. Residuals were extracted and compared to the calpastatingenotypes. These were analysed using an analysis of variance to obtainadjusted means for each genotype Plots of raw and residual peak-forcevalues against calpastatin genotypes were constructed.

EXAMPLE 2 Analysis of CAST3 D/E Polymorphism

There are two common alleles (FIG. 1) and at least one rare allele forthe CAST D/E polymorphism and both the common alleles are found in allthe breeds, although there are clear differences in genotype frequencywithin the breeds. Zebu breeds have a greater frequency of the ‘11’genotype (Tables 2 and 7) than taurine breeds in this sample.

The raw values (FIGS. 2 a& 2 b) were then plotted against the CASTgenotypes (FIGS. 3 a& 3 b) and these associations are sufficientlystrong to show visual associations between peak-force and genotype. Themost important genetic effect considered in the literature for CAST,breed or taurine versus zebu, has been carefully matched so that thereare animals of high and of low peak-force from each breed in the sample,and breed is not expected to be an explanatory variable here.

The analysis (Table 3) of the CAST genotypes shows strong, confirmatoryevidence of effects of the CAST gene or sequences near the CAST gene onpeak-force. The analysis shows no effect of breed, but since the sampleconsists of individuals of high and low peak-force for each breed, thiswas not unexpected. The size of effect associated with this polymorphismis approximately 7.9 percent of the phenotypic variance estimated as amain effect, and the deviance associated with CAST genotype nestedwithin breed within region is 121.4 (17 df, P=0.001894). An un-nestedinteraction term between breed and CAST genotype was calculated for thissample, but is was not statistically significant. The GLM of the CASTgenotypes (Table 4) against the residual peak force measurements show astatistically significant level of association similar to that of theCAST genotypes considered as a main effect (Table 3) rather than whenthey are nested within region and breed.

A boxplot of genotypes versus the residual peak force measurements (FIG.4) shows clear differences in distributions and the difference betweenmedians of the ‘11’ and the ‘22’ genotypes are approximately 1.2 kg ofadjusted peak force. The adjusted means from the analysis of variance(Table 5) gives a difference of 1.34 kg of peak force between thehomozygote genotypes. The overall standard deviation for the residualsis 1.61.

The GLM of the confirmatory sample of 77 animals showed a statisticallysignificant association between CAST genotype and peak force, with the‘1’ allele associated with higher peak forces. When the full model wascalculated, none of the factors were statistically significant, possiblyas a result of the relatively small sample size. Terms in the model weredropped one by one using the reduction in AIC as the criterion. Allterms except the calpastatin genotypes were dropped (Table 8) in thisautomatic procedure, and these show a deviance of 17.9 (2 df, P<0.05)explaining 9.5 percent of the phenotypic variance. This is similar tothe 8.9 percent found when the CAST genotypes were compared withoutother factors to peak force in the first sample.

Discussion

The results presented here indicate that genetic variation at the CASTgene is important in explaining variation for Warner-Bratzler peak-forcemeasurements between individuals irrespective of the breed of origin.The sample was chosen to control for the effects of breed and to spreadthe sample as widely as possible over different sire lines, contemporarygroups, feeding and finishing regimes; care was taken to ensure that, asmuch as possible, individuals in either extreme were from each breed,contemporary group, feeding and finishing system. in this way,systematic effects of these factors on peak-force were controlled sothat the effect of the alleles would not be due to inadvertently beingcarried along by other factors affecting peak-force values. Indeed,there are statistically significant deviations in peak-force due toallelic substitution at this locus even when there is no accounting forthe other fixed effects. Inspection of the raw data show frequencydifferences within breeds for the different genotypes so that the ‘1’allele is rarer in the extreme with lower peak-force values.

This association between the ‘1’ allele and higher peak forcemeasurements is confirmed in a second smaller sample of extreme animals.These animals are less extreme than those in then first sample, they arethe left-over extremes, and they clearly show not only that thecalpastatin genotypes are important but that in such a small sample,other factors known to be important are not found to be statisticallysignificant. In a well matched sample such as this it is not of concern,since we attempt to remove the effects of the other factors as much aspossible through the choice of samples to analyse.

The size of the homozygote substitution is approximately 1.34 kg of peakforce for the LD, equivalent to 0.83 of standard deviation. This valueis likely to be overestimated since the extremes of the distributionwere used, and a proper estimate will require animals chosen at randomfrom the full distribution of peak force. Nevertheless, this is a usefulamount of genetic variance associated with a single marker and it isexpected that this marker would be useful in direct DNA marker tests forbreeding and feedlot streaming.

The analysis shows no evidence of a breed by genotype interaction onpeak-force, which means that there is no evidence that the alleleassociation is different or absent in some breeds. This is interpretedto mean that there is no heterogeneity in the breeds for the associationbetween calpastatin and peak-force.

A positive test for allelic association generally means that thecausative mutation is close to the DNA markers. Associations in otherstudies have indicated that allelic association decays at an extremelyrapid rate so that DNA markers even relatively close to a quantitativetrait locus will find no evidence of association (e.g., Coleman et al.,1995; Barendse, 1997). This indicates that the causative mutation ormutations are extremely close to the CAST D/E polymorphism.

EXAMPLE 3 Identification of CAST5 Microstatellite Polymorphism

To determine whether other polymorphisms in the CAST gene are associatedwith tenderness, both of the cattle samples (Tables 1 & 6) weregenotyped with the CAST5 microsatellite polymorphism (Nonneman et al,1999) which was developed from DNA sequence reported earlier (Cong etal., 1998).

The primer sequences to amplify this polymorphism are

CAST5U1: 5′-GTAAAGCCGCACAAAACACACCCAGG-3′ and (SEQ ID NO:7) CAST5D1:5′-GTTTCTGGACCCTCTGGATGAGGAAGCGG-3′ (SEQ ID NO:8)with amplification fragments in the range 130- 159 bp, sizes determinedon an ABI 373 DNA sequencer. Alleles and genotypes were assigned basedon these size fragments leading to 13 alleles and the distribution ofallele sizes is shown in FIG. 5.

Two different sets of analyses were performed. In the first, thegenotypes at the CAST3 D/E polymorphism were compared to the CAST5microsatellite to determine whether there were significant associationsbetween the genotypes, as a consequence of haplotypes existing along theDNA sequence. If CAST5 and CAST3 show significant haplotypes, since theyare on either side of the CAST coding sequence, then all polymorphismsfor the CAST coding sequence are expected to be in linkagedisequillibrium with either or both of these DNA markers. In the second,the CAST5 microsatellite alleles were compared to the LD peak-forcemeasurements to determine whether there was any association betweenCAST5 and tenderness.

Haplotypes Between CAST5 and CAST3

Since genotypes of parents of these animals were not availablehaplotypes where determined by analysing animals in which one or both ofCAST5 and CAST3 had homozygous genotypes. The frequency of thesehaplotypes were tabulated (Table 9). These frequencies were tested forheterogenity using a generalised linear model and found to be highlyheterogenous (Table 10). This means that each allele at CAST3 D/E ispreferentially associated with specific alleles at the CAST5microsatellite.

Association Between Tenderness and CAST5

Since CAST5 has 13 alleles and hence there are 91 possible genotypes,not all of these genotypes will be seen in a sample of 240 samples, asin this study, so the association was estimated on the alleles. As forthe CAST3 D/E DNA marker, the polymorphism was compared to the raw LDpeak-force values (Table 11 a), was examined for differences ininteractions between breeds (Table 11 b), and was compared to the LDpeak-force values after market, cohort, breed and finish effects wereaccounted for (Table 11 c). In the last of these analyses, CAST5 allelesare nested within finish and breed, as in the analysis of CAST3.

These analyses show that there is no interaction between CAST5 allelefrequency and breed on LD peak-force, that the association between CAST5and the raw LD peak-force values is statistically significant at thethreshold P<0.01, but when the CAST5 alleles are nested within breed andfinish, the association has a deviation which is 0.1>P>0.05. The lack ofinteraction between CAST5 and breed in explaining LD peak-force meansthat any differences in gene frequencies between breeds are notresponsible for the association between CAST5 and LD peak-force. Theassociation between CAST5 and LD peak-force in sections a and b of Table11 indicate that there is some evidence for CAST5 associated with LDpeak-force, but a bias might still exist, which is why the factors suchas market, cohort, breed and finish are also corrected for. Once thosefactors are corrected, there is a lack of strength in the association.In the CAST3 D/E analysis, correcting the additional factors improvedthe evidence for the association, and since the same samples are used,we know in which direction the deviations should go. Thus the lack ofstrength probably means that the large number of alleles nested withinbreed and finish, has failed to find an association due to the creationof a large number of categories. Larger numbers of alleles are expectedto reduce the strength of associations purely due to the number ofcategories (of Terwilliger, 1995).

The CAST5 polymorphism can be used in conjunction with the CAST3 D/Epolymorphism to predict LD peak-force. For CAST3 D/E the c11 genotype isassociated with higher peak force values, the c12 genotype isintermediate and the c22 genotype has the lower peak force values.Secondly, there is linkage disequillibrium between CAST3 D/E and CAST5.By examining the table of haplotypes, looking at the commonmicrosatellite alleles, CAST3 D/E al (allele 1) is most often associatedwith CAST5 m3 (allele 3) with low abundances for m7 and m9. On thecontrary, CAST3 D/E a2 (allele 2) is most often associated with CAST5m9, with a similar large association to m3 and a lesser but stillsignificant association with m7. Inspection of FIG. 6, a plot of raw LDpeak-force values for each CAST5 microsatellite allele, indicates thatCAST5 m7 and m9 have lower peak force values while CAST5 m3 has higherpeak force values. Since most of the m3 alleles are actually associatedwith CAST3 D/E a2 and not a1 (108 versus 14), this higher value is notlikely to be the effect of CAST D/E a1. Rather it provides a tool torefine the assignment of animals to groups, since animals selected forhaving CAST3 D/E a2, so that they might have lower peak force values,might still have higher peak force values if they possessed CAST5 m3.They are expected to have a greater likelihood of having lower peakforce values if they possessed both CAST3 D/E a2 as well as CAST5 m7 orm9.

EXAMPLE 4

This example shows the testing of a DNA marker in the LOX gene forpopulation associations to STIC and STADH. Repeated statisticallysignificant positive associations were found between genotypes and bothSTIC and STADH. These indicate that, unusually, the heterozygote may beone of the extreme genotypes suggesting some overdominance. Theseassociations are found in a study of 6 breeds of cattle with a structureto detect linkage disequilibrium and would indicate that the gene LOXeither contained or was located near to the genetic factor associatedwith connective tissue strength.

Materials and Methods

Cattle were chosen from the CRC DNA Bank to have as diverse a geneticand phenotypic background as possible. Two groups of animals werechosen, the first and larger set to test for associations and the secondsmaller set to confirm the polarity of the associations (cf. Barendse1997; Barendse et al., 2000). Information stored in the CRC Database wasused to select animals. Animals of extremes of instron compression inthe semitendinosis muscle were selected. Adhesion measures for theseanimals were also extracted from the database. In essence, the procedurewas to select cattle in each cohort which were of phenotypic extrememeasures, to ensure that no sire was represented by a cluster ofoffspring, that all markets and finishing regimes were included in eachextreme, so that extremes were not biased by being representative of aparticular market or finishing regime. A total of 253 individuals wereselected comprising a first sample of 166 animals and a second sample of87 animals (Table 11).

The DNA was genotyped for the LOX (Lysyl Oxidase) DNA fragment using theprimers LOX K5: 5′ tat cac tga tgt caa acc tg 3′ (SEQ ID NO:9)and LOXK6: 5′ act cag gca cca aat agc tg 3′ (SEQ ID NO:10). The conditions ofthe polymerase chain reaction (PCR) are an annealing temperature of 60Celsius, 2.5 mM Magnesium chloride, and reagent mixes obtained fromBiotech International. The DNA fragments were labeled via theincorporation of ³²P dCTP into the fragments during the PCR. Thefragments were digested with HinfI overnight at 37 Celsius beforeseparation on gels. The fragments were visualised via autoradiography toX-Ray film overnight at room temperature. Alleles were scored innumerical order where the fastest migrating allele is number 1.

The genotypes were analysed using generalised linear models (GLM)following the equation STIC=1+genotypes nested within fixedeffects+error implemented via the S-PLUS software. The same model isused for STADH. Fixed effects that were considered were breed, finish(Domestic, Korea, Japan), cohort, region (pasture v grain, north vsouth) and the covariate of age. Age was included since LOX affectscross-linking of collagen and cross-linking is an age related process,with cross-linking increasing over time. The genotypes were nestedwithin region and breed since pure-bred offspring of taurine sires werenot pastured in the north. The size of the effects associated withgenotype was estimated by the comparison of variances (eg,Andersson-Eklund and Rendel, 1993). To estimate the size of effectassociated with genotypic substitution, the same model was fittedwithout the LOX genotypes. Residuals were extracted and compared to theLOX genotypes. These were analysed using an analysis of variance toobtain adjusted means for each genotype.

Results

There are two alleles (FIG. 7) for the LOX polymorphism and both thesealleles are found in all the breeds, although there are cleardifferences in genotype frequency within the breeds. There is noconsistent difference between zebu and taurine breeds in frequency ofthe genotypes (Table 12). The Hereford breed differs radically ingenotype frequencies to all the other breeds in the sample. It has highfrequencies of genotype ‘22’ while all other breeds have highfrequencies of genotype ‘11’.

The STIC and STADH values are correlated with R=0.52 (FIGS. 8-10). Theplots indicate that while the STIC values show two clear extremes theSTADH values have only a long tail and do not show two discretedistributions. This reflects that the sample was selected only on STIC.

The analyses (Tables 13 and 14) of LOX against STIC and STADH showconsistent statistically significant associations. The first and thesecond samples as well as the combined samples of both STIC and STADHshow associations to LOX genotypes at P<0.05. The association of LOXappears stronger to STADH than to STIC. The association in the secondsample of STADH phenotypes has extremely high statistical significancebut this may be due to sampling in small populations and the congruenceof extreme phenotypes with particular genotypes. The combined analysisof STADH is less extreme than the second sample but shows confirmatorylinkage to LOX (P<0.01).

Nevertheless, it is clear that these are not large genotypicsubstitution effects and some analyses do not show statisticalsignificance. When the LOX genotypes were compared to residual STIC andSTADH, none of these associations was statistically significant, whetherby sample or data combined (Table 15). Interestingly, some of thecomparisons show that the heterozygotes are of extreme phenotype,opening the possibility of overdominance at this locus. This will needto be confirmed using other polymorphisms at the LOX gene that showlarger genotypic substitution effects.

Discussion

Consistent with those earlier analyses, the STADH values show greaterassociation to the DNA marker than the STIC values, even though thesamples are extreme for STIC, with STADH values only more dispersed thannormal due to the correlation between traits (FIG. 9). STIC was chosenupon which to select extremes rather than STADH. However, both of thesemeasurements evaluate aspects of connective tissue strength, theadhesion measures the force required, in crude terms, to pull a muscleapart, the force applied perpendicular to the fibre bundles, while theinstron compression measures how much the muscle can be flattenedwithout being torn or cut. These are not perfectly correlated as can beseen by inspection of the distribution of STIC and STADH values (FIG.10).

EXAMPLE 5

The association between the marker and STIC was examined in Example 4using two batches of extreme animals. The results show that there aresignificant associations between the genotypes of the marker and STIC(instrom compression, P<0.05), and STADH (adhesion, P<0.01). The resultssuggest that the gene LOX either contains or is located near the geneticfactor associated with connective tissue strength.

Because this study was carried out on a relative small population (253)with extreme animals only, the same marker was tested on differentpopulations to see if the association is still valid.

Materials and Methods

In addition to the population, there are two other groups containinganimals chosen from the two tails of instron compression (LDIC, 136) andpeak force (LDPF, 131) for the LOX gene study. These three extremegroups together with 559 non-extreme individuals form the base for theseanalyses on the LOX marker. A total of 917 individuals were used for thestudy (Table 16).

Due to the nature of the populations, the analyses were carried out tothe three datasets.

-   -   Extreme animals only (389). The extreme animals from LDIC, LDPF        and STPF were pooled together.    -   Non-extreme animals (559).    -   Combined data (917). The combination of 1 and 2.

Beside the traits STIC, LDPF and LDIC, a range of other traits was alsoevaluated to see if there is any effect of LOX gene on other meatquality traits (Table 17). The intramuscular fat measurements fromLD_FAT % and NIR_FAT % were combined to make a single trait.

The mixed model procedure (MLX) in SAS (version 8.0) was used to run thestatistical analyses. The fixed effects in the model include finishgroup and LOX marker. Sire and contemporary groups are treated as randomeffects. All these effects were nested within individual breeds. Thestatistical model used is as follow:

Trait=mean+sire within breed+contemporary group within breed+finishwithin breed+LOX within breed+Carcass weight.

Contemporary group was defined as the combination of herd of origin,cohort and kill code. The individuals without electrical stimulationwere removed from the analysis data. Carcass weight is being used as acovariate to adjust for the age difference.

A full contrast model would be performed if a significant marker-traitassociation was identified from a mixed model (or GLM) analysis. Thepurpose of conducting such the test is to further examine thepossibility of additive or dominance or overdominance effect among thegenotypes of the LOX marker. The full contrast of 3 genotypes (11, 12and 22) is set up in SAS as follow:

-   -   contrast ‘Additive Test’ lox(bcode) 1 0 −1;    -   Contrast ‘Homozygotel vs Heterozygote’ 1 −1 0    -   Contrast ‘Heterozygote vs Homozygote2’ 0 1 −1    -   contrast ‘Dominance Test’ lox(bcode) −1 2 −1;    -   contrast ‘Recessive Test’ lox(bcode) −1 −1 2;    -   contrast ‘OverDominance Test’ lox(bcode) 2 −1 −1;

For the extreme population in which the animals with extreme phenotypeswere genotyped, multi-trait logistic regression method (Henshall andGoddard, 1999) was applied to take the potential correlation of traitsinto account. The method is regression based, but instead of regressingphenotype on genotype, the regression is genotype on phenotype. Thisreplaces the assumption that phenotypes are unselected with theassumption that there was no selection based on genotypes (Henshall andGoddard, 1998). Prior to using logistic regression method, MLX model wasused to all data (917 animals) to derive predicted values of individualanimals. The predicted phenotype values for the extreme animals afteradjusting for significant fixed effects were then used for Logisticregression analyses. The analyses started with single trait logisticregression method and then proceeded to multi-trait logistic regressionmethod.

The genotype frequency distribution of the marker in differentpopulations is shown in Table 18. From the table, it can be seen thatthe Hereford breed differs remarkably in genotype frequencies to all theother breeds in the populations. In order to investigate the potentialeffect of skewed genotypes of Hereford breed on the overall results, aset of additional analyses were also pursued to the populations byremoving the Hereford individuals from the data sets.

Results and Discussions

Part I. Extreme Animals (Table 19)

Extreme Animals for LDIC

The first test was conducted to the sample containing the selectedanimals for LDIC (136). The results from the analysis of variance revealthat there was no close association between any genotypes of LOX markerand LDIC. The same conclusion was held to other meat quality traits.

Extreme Animals for LDPF

Like LDIC sample, there was no significant variation detected betweenthe LOX marker and any meat quality trait in the batch animals selectedfor LDPF. The results are not surprising as the initial QTL fortenderness in CBX experiment was identified in instron compressionmeasurement of Semitendinosus muscles.

Combined Extreme Animal Data

Analysis of Variance. As sire effect was confounded with other effects,it had to be removed from the model and GLM (generalised linear model)was performed. In this case, contemporary group was treated as a fixedeffect rather than a random effect. The analysis of variance has shownthat out of 21 meat quality traits tested, STIC and LDL had significantresults (P<0.05).

Full Contrast Model. The results from full contrast model are givenbelow. For STIC, it can be seen that there was no additive effectbetween the two homozygous genotypes (11 and 22). However, the highlysignificant difference between the phenotypes of 11 and 12 obviouslycontributed to the detection of dominant and overdominant effects.Nothing was remarkable for LDL.

Logistic Regression. After adjusting for the significant fixed effectson all data, logistic regression was applied to STIC. Multi-traitlogistic regression model was also fitted to take the potentialcorrelations between ST measurements into account (STIC, STPF andSTADH). The results confirm the findings from the other methods. Thatis, LOX genotypes did have a correlation with STIC. The regressionco-efficiency between lox marker and STIC is shown in the output oflogistic procedure (below). The allele substitution effect of the loxmarker could be derived from the formulae suggested by Henshall andGoddard (1999) based on the total variance of whole data. Themulti-trait logistic regression test on STIC, STPF and STADH has shownthat both STIC and STPF had significant effects on LOX gene marker. STPFwas marginally non-significant in GLM analysis. (Table 20)

Part II. CRC Non-Extreme Animals

The non-extreme animals (559) were genotyped against LOX marker in CRC Iand but were not part of the animals involved in marker evaluationPhases III. The mixed model analyses of variance show that beside STIC,the significant marker-trait association was also detected to theintramuscular fat (FAT) and LDPH. However, full contrast test to STICand FAT has failed to pinpoint the genotype causing the significantresults. In the case of LDPH, it seems that 22 genotype had an importantrole in determining the outcomes. (Table 21)

Part III. Combined Data

When extreme and non-extreme animals were pooled together, the resultsfrom mixed model analysis of variance show that again the lox marker wasassociated with STIC (P<0.05). The significant results were also foundin STL, which is the measurement of darkness of carcass meat colour.However in both cases, full contrast model had not be able to identifythe significant genotype sources. (Table 22)

Part IV. Removing Hereford Individuals from the Combined Population

In order to test the possible effect of skewed distribution of loxgenotypes of Hereford breed, the additional analyses were also performedto the combined data with the removal of Hereford breed. The resultsindicate that the removal of Hereford animals has changed little to theoverall significant results of STIC in the combined population. From thegenotype frequency distribution table, it can be seen that the majorityof Hereford individuals were from the three extreme populations exceptone animal from non-extreme CRC population. (Table 23)

The overall results from the investigation of LOX gene effect on meatquality traits have been consistent across three populations (extreme,non-extreme and combined). That is, there is a strong association of LOXgene marker with the instron compression measurement of Semitendinosusmuscles (P<0.05). The significant results from other meat quality traitsvary from one population to another.

INDUSTRIAL APPLICABILITY

The invention is useful in allowing selection and breeding of animalswhich yield more tender meat.

TABLE 1 Characteristics of the first Cattle Sample Total: 169 83 highpeak force 86 low peak force Breeds: 29 Santa Gertrudis 25 Hereford 26Angus 27 Belmont Red 31 Brahman 31 Shorthorn Regions: 38 Pasture South28 Pasture North 57 Grain South 41 Grain North Markets: 72 Korean 67Domestic 25 Japanese Cohorts: 27 Cohorts Median: 5 steers per cohortbottom quartile: 2 steers per cohort top quartile: 9 steers per cohortSires: 112 sires Median: 1 steer per sire bottom quartile: 1 steer persire top quartile: 2 steers per sire

TABLE 2 Distribution of CAST genotypes in the breeds in the firstsample. Genotype Breed 11 12 22 23 Angus 0 7 19 0 Belmont Red 0 8 19 0Brahman 6 13  10 2 Hereford 0 5 17 0 Santa Gertrudis 3 5 19 0 Shorthorn0 4 23 0

TABLE 3 Associations between calpastatin genotypes (castg) andtenderness. A. Calpastatin by itself Analysis of Deviance Table Gaussianmodel Response: peakforce Terms added sequentially (first to last) DfDeviance Resid. Df Resid. Dev F Value Pr(F) NULL 155 864.6307 castg 370.89899 152 793.7317 4.52573 0.004536025 B. Breed × CalpastatinInteractions Analysis of Deviance Table Gaussian model Response:peakforce Terms added sequentially (first to last) Df Deviance Resid. DfResid. Dev F Value Pr(F) NULL 155 864.6307 finish 2 101.8455 153762.7852 15.97637 0.0000008 cohort 25 273.5882 128 489.1971 3.433390.0000041 region 3 59.6620 125 429.5350 6.23940 0.0005889 breed 410.8711 121 418.6639 0.85267 0.4948672 castg 3 28.2709 118 390.39302.95654 0.0355308 breed:castg 6 33.4066 112 356.9864 1.74682 0.1166518C. Calpastatin genotypes nested with breed and region Analysis ofDeviance Table Gaussian model Response: peakforce Terms addedsequentially (first to last) Df Deviance Resid. Df Resid. Dev F ValuePr(F) NULL 155 864.6307 finish 2 101.8455 153 762.7852 18.288420.0000002 cohort 25 273.5882 128 489.1971 3.93027 0.0000005 region 359.6620 125 429.5350 7.14235 0.0002128 breed in region 7 26.8943 118402.6408 1.37983 0.2219802 castg in (region/breed) 17 121.4139 101281.2269 2.56498 0.0018938

TABLE 4 Analysis of CAST against residual peakforce measurements (X1).Analysis of Deviance Table Gaussian model Response: X1 Terms addedsequentially (first to last) Df Deviance Resid. Df Resid. Dev F ValuePr(F) NULL 154 402.3160 castg 3 21.97947 151 380.3365 2.90874 0.03654659Call: glm(formula = X1 castg, data = calppftest, na.action = na.omit)Coefficients: (Intercept) castg1 castg2 castg3 0.07693593 −0.4095948−0.3102917 −0.3504961 Degrees of Freedom: 155 Total; 151 ResidualResidual Deviance: 380.3365 Model from which residuals were calculatedAnalysis of Deviance Table Gaussian model Response: peakforce Termsadded sequentially (first to last) Df Deviance Resid. Df Resid. Dev FValue Pr(F) NULL 160 906.5175 finish 2 106.0217 158 800.4958 15.522930.0000010 cohort 26 290.8387 132 509.6571 3.27558 0.0000057 finlwt 18.0662 131 501.5908 2.36200 0.1269337 region 3 63.9431 128 437.64786.24139 0.0005629 breed in region 7 24.4323 121 413.2154 1.022060.4192678 glm(formula: peakforce = finish + cohort + finlwt +region/breed, data = calppf, na.action = na.omit)

TABLE 5 Analysis of Variance tables between CAST genotypes and residualpeak force measures (X1) along with the table of adjusted meansassociated with each genotype. Analysis of Variance Table Response: X1Terms added sequentially (first to last) Df Sum of Sq Mean Sq F Value Pr(F) castg 3 21.9795 7.326490 2.90874 0.03654659 Residuals 151 380.33652.518785 Tables of adjusted means Grand mean 0.076936 se 0.318703 castgc11 c12 c22 c23 1.1473 0.3281 −0.1932 −0.9746 se 0.5290 0.2479 0.15641.1222

TABLE 6 Characteristics of the second sample of 77 animals. Total: 77 39high peak force 38 low peak force Breeds: 11 Belmont Red 11 Hereford 13Brahman 13 Shorthorn 14 Santa Gertrudis 15 Angus Regions: 24 PastureSouth 12 Pasture North 21 Grain South 20 Grain North Markets: 35 Korean25 Domestic 17 Japanese Cohorts: 22 Cohorts Median: 3 steers per cohortbottom quartile: 2 steers per cohort top quartile: 5 steers per cohortSires: 64 sires Median: 1 animal per sire bottom quartile: 1 animal persire top quartile: 1 animal per sire

TABLE 7 Distribution of CAST genotypes in the second sample GenotypeBreed 11 12 22 Angus 0 3 12  Belmont Red 0 3 8 Brahman 3 7 3 Hereford 03 8 Santa Gertrudis 1 4 9 Shorthorn 0 0 13 

TABLE 8 Associations between calpastatin genotypes and LD peak force inthe second sample. Analysis of Deviance Table Gaussian model Response:ldpeakforce Terms added sequentially (first to last) Df Deviance Resid.Df Resid. Dev F Value Pr(F) NULL 76 205.9332 castg 2 17.90313 74188.0300 3.522925 0.03455689 Coefficients: (Intercept) castg1 castg25.227591 −0.1205 −0.3719088 Degrees of Freedom: 77 Total; 74 ResidualResidual Deviance: 188.03 Single term deletions Model: ldpeakforce =lslortwait + buttemp + finish + cohort + region + breed + castg FinalCall: glm(formula = ldpeakforce castg, data = calppfr, na.action =na.omit) Coefficients: (Intercept) castg1 castg2 5.195064 −0.07055556−0.37438 Degrees of Freedom: 67 Total; 64 Residual Residual Deviance:161.5878

TABLE 9 The amount of each haplotype found between the alleles of thecast5 microsatellite and the cast3 D/E SNP on both cattle samples.Twenty-six haplotypes were found in animals that are homozygous for oneor the other locus. allele haplotype cast3 cast5 amount 1 a1 m1 3 2 a1m2 0 3 a1 m3 14 4 a1 m4 0 5 a1 m5 0 6 a1 m6 3 7 a1 m7 6 8 a1 m8 2 9 a1m9 6 10 a1  m10 5 11 a1  m11 0 12 a1  m12 1 13 a1  m13 0 14 a2 m1 0 15a2 m2 1 16 a2 m3 108 17 a2 m4 1 18 a2 m5 4 19 a2 m6 3 20 a2 m7 42 21 a2m8 2 22 a2 m9 110 23 a2  m10 17 24 a2  m11 6 25 a2  m12 1 26 a2  m13 1

Table 10 A heterogeneity test for associations between alleles at CAST5with alleles at CAST3 D/E. Analysis of Deviance Table Poisson modelResponse: score Terms added sequentially (first to last) Df DevianceResid. Df Resid. Dev Pr(Chi) NULL 25 926.9836 cast3  1 220.4994 24706.4842 0.0000000000 cast5 12 671.7497 12  34.7345 0.0000000000cast3:cast5 12  34.7344  0  0.0001 0.0005161421 Table 11 Tests forassociation between CAST 5 microsatellite and LD peak force measurementsin both cattle samples. Part A. Calpastatin by itself Analysis ofDeviance Table Gaussian model Response: ldpf Terms added sequentially(first to last) Df Deviance Resid. Df Resid. Dev F Value Pr(F) NULL 4912266.640 ma111 12 136.0104 479 2130.629 2.548113 0.002843108 Part B.Breed by calpastatin interactions Analysis of Deviance Table Gaussianmodel Response: ld Terms added sequentially (first to last) Df DevianceResid. Df Resid. Dev F Value Pr(F) NULL 491 2266.640 market  2 218.4756489 2048.164 42.35776 0.0000000 cohort 26 706.3980 463 1341.766 10.535040.0000000 finish  3  97.7159 460 1244.050 12.63003 0.0000001 breed  4 23.5779 456 1220.472  2.28562 0.0594827 cast5 12  65.7617 444 1154.711 2.12497 0.0146070 breed:cast5 26  76.7170 418 1077.994  1.144140.2865614 Part C. Calpastatin genotypes nested with breed and finish(region) Analysis of Deviance Table Gaussian model Response: ld Termsadded sequentially (first to last) Df Deviance Resid. Df Resid. Dev FValue Pr(F) NULL 491 2266.640 market  2 218.4756 489 2048.164 42.901210.00000000 cohort 26 706.3980 463 1341.766 10.67020 0.00000000 breed  4 30.7280 459 1311.038  3.01697 0.01799825 finish % in % reed  6 111.2067453 1199.832  7.27908 0.00000022 cast5 % in % (breed/finish) 65 211.8811388  987.950  1.28019 0.08307834

TABLE 11 Characteristics of the Cattle Sample Total: 166 87 high instroncompression 87 39 low instron compression 89 38 Breeds: Angus 25 12Belmont Red 25 12 Brahman 33 18 Hereford 32 10 Santa Gertrudis 26 15Shorthorn 25 17 Regions: Pasture South 47 20 Pasture North 39 21 GrainSouth 43 20 Grain North 37 26 Markets: Korean 81 22 Domestic 47 45Japanese 38 22 Cohorts: 25 14 Median: steers per cohort 6 3 bottomquartile: 3 1 top quartile: 10 11 Sires: 113 62 Median: steers per sire1 1 bottom quartile: 1 1 top quartile: 2 2

TABLE 12 Distribution of LOX genotypes in the breeds in the combinedsample. Genotype Breed 11 12 22 Angus 12 16 5 Belmont Red 19 14 2Brahman 20 21 4 Hereford  1  7 27  Santa Gertrudis 18  5 1 Shorthorn 2311 3

TABLE 13 Associations between LOX genotypes (loxg) and STIC. A. FirstSample Analysis of Deviance Table Gaussian model Response: stic Termsadded sequentially (first to last) Df Deviance Resid. Df Resid. Dev FValue Pr(F) NULL 144 66.41782 market 2 4.68851 142 61.72932 10.113460.0001124 age 1 0.33061 141 61.39871 1.42631 0.2356143 cohort 2325.50396 118 35.89474 4.78382 0.0000000 region 3 2.05111 115 33.843632.94960 0.0371506 breed % in % region B 3.73686 107 30.10677 2.015170.0537829 loxg % in % (region/breed) 20 9.94057 87 20.16620 2.144250.0081786 B. Second Sample Analysis of Deviance Table Gaussian modelResponse: stic Terms added sequentially (first to last) Df DevianceResid. Df Resid. Dev F Value Pr(F) NULL 77 36.73118 market 2 18.56015 7518.17103 50.57448 0.0000000 age 1 1.22303 74 16.94800 6.66525 0.0131586region 3 0.49228 71 16.45571 0.89428 0.4514796 breed % in % region 92.26136 62 14.19435 1.36933 0.2303598 loxg % in % (region/breed) 175.93715 45 8.25720 1.90331 0.0433708 NOTE: cohort could not be fitted asit required a model with more terms than degrees of freedom. cohort wasdropped since that allowed a maximum of other terms to be fitted. C.Combined Sample Analysis of Deviance Table Gaussian model Response: sticTerms added sequentially (first to last) Df Deviance Resid. Df Resid.Dev F Value Pr(F) NULL 222 105.0131 market 2 9.28932 220 95.723814.63402 0.0000015 age 1 0.66404 219 95.0598 2.09221 0.1500079 cohort 2328.46167 196 66.5981 3.89890 0.0000002 region 3 0.97412 193 65.62401.02306 0.3840448 breed % in % region 9 2.08684 184 63.5372 0.730560.6804229 loxg % in % (region/breed) 24 12.75507 160 50.7821 1.674480.0327546

TABLE 14 Associations between LOX genotypes (loxg) and STADH. A. FirstSample Analysis of Deviance Table Gaussian model Response: stadh Termsadded sequentially (first to last) Df Deviance Resid. Df Resid. Dev FValue Pr(F) NULL 139 4.118160 market 2 0.045611 137 4.072549 1.636650.2008490 age 1 1.030876 136 3.041674 73.98193 0.0000000 cohort 231.131428 113 1.910246 3.53035 0.0000129 region 3 0.053546 110 1.8566991.28094 0.2863452 breed % in % region 8 0.192061 102 1.664639 1.722930.1050769 loxg % in % (region/breed) 19 0.508104 83 1.156535 1.919190.0229812 B. Second Sample Analysis of Deviance Table Gaussian modelResponse: stadh Terms added sequentially (first to last) Df DevianceResid. Df Resid. Dev F Value Pr(F) NULL 76 3.362345 market 2 0.560443 742.801903 25.18352 0.0000000513 age 1 0.486387 73 2.315516 43.711640.0000000425 region 3 0.390962 70 1.924554 11.71193 0.0000090738 breed %in % region 9 0.413632 61 1.510922 4.13035 0.0006637134 loxg % in %(region/breed) 17 1.021326 44 0.489595 5.39922 0.0000032684 C. CombinedSample Analysis of Deviance Table Gaussian model Response: stadh Termsadded sequentially (first to last) Df Deviance Resid. Df Resid. Dev FValue Pr(F) NULL 216 7.480742 market 2 0.268508 214 7.212234 7.787090.0005990 age 1 1.252352 213 5.959882 72.63985 0.0000000 cohort 232.111912 190 3.847971 5.32594 0.0000000 region 3 0.065737 187 3.7822331.27098 0.2863505 breed % in % region 9 0.325818 178 3.456415 2.099820.0326124 loxg % in % (region/breed) 23 0.784128 155 2.672287 1.977460.0079540

TABLE 15 Estimated sizes of effects of genotype substitutions at LOX oninstron compression and adhesion of the semitendinosus muscle. Response:stic.resid Grand-mean 0.027133 se 0.053648 Loxg 111 112 122 0.03958−0.02321 0.06503 se 0.07654 0.07897 0.11751 Response: sticbox3.residGrand-mean −0.012888 se 0.039137 Loxg 111 112 122 0.033317 −0.058769−0.013213 se 0.055916 0.058431 0.085115 Response: sticfull.residGrand-mean −0.012888 se 0.039137 loxg 111 112 122 0.033317 −0.058769−0.013213 se 0.055916 0.058431 0.085115 Response: sticadh.residGrand-mean 0.002521 se 0.010321 Loxg 111 112 122 −0.001426 −0.0023040.011292 se 0.014860 0.015390 0.022384 Response: sticbox3adh.residGrand-mean 0.004308 se 0.012879 loxg 111 112 122 −0.003154 −0.0074460.023524 se 0.018594 0.018891 0.028111 Response: sticfulladh.residGrand-mean 0.0037399 se 0.0092835 loxg 111 112 122 −0.009303 −0.0052440.025767 se 0.013379 0.013763 0.020180

TABLE 16 Information on the data sets Combined Non- Effect Class LDICLDPF Extreme extreme Combined Total 136 131 398 543 916 Sires 96 96 17161 227 Cohorts 24 24 26 10 30 Breeds Angus 19 22 62 134 196 Belmont 2325 66 140 200 Red Brahman 24 27 76 73 142 Hereford 27 14 67 1 68 Santa25 24 73 195 257 Gertrudis Shorthorn 18 19 53 0 53 Regions Pasture 27 2792 93 185 South Pasture 24 25 78 144 212 North Grain 54 41 125 168 291South Grain 31 38 102 138 228 North Markets Domestic 47 51 130 240 361Korean 61 58 189 201 376 Japaness 28 22 78 102 179

TABLE 17 Meat quality traits tested for LOX gene marker Code TraitLD_Fat % Intramuscular Fat percentage (Soxhylet Method) LD_ICLongissimus dorsi Instrom compression LD_IY Longissimus dorsi initialyield (Nth kills only) LD_LOSS Longissimus dorsi cooking loss % LD_PFLongissimus dorsi Peak Force - must use “Stim” also LD_PF-IY Longissimusdorsi Peak Force - initial yield (Nth) LD_a Longissimus dorsi a* colourLD_b Longissimus dorsi b* colour LD_1 Longissimus dorsi L* colour LD_pHLongissimus dorsi ultimate pH NIR_Fat % Intramuscular Fat percentage(NIR method) ST_AdhRS Semitendinosus Shorthose adhesion ST_ICSemitendinosus Instrom compression ST_IY Semitendinosus initial yield(Nth kills only) ST_LOSS Semitendinosus cooking loss % ST_PFSemitendinosus Peak Force ST_PF-IY Semitendinosus Peak Force - initialyield (Nth) ST_a Semitendinosus a* colour ST_b Semitendinosus b* colourST_1 Semitendinosus L* colour ST_pH Semitendinosus ultimate pH TenderQTenderness Quality as measured by PF (× 100)

TABLE 18 Distribution of LOX genotypes in the breeds in the threedatasets Extreme Non-extreme Combined Breed 11 12 22 11 12 22 11 12 22Angus 21 33 7 38 75 21 59 109 28 Brahman 35 35 6 19 40 14 52 71 19Belmont 31 23 12 59 60 21 87 83 30 Red Hereford 4 17 46 0 1 0 4 18 46Santa 42 29 2 120 62 13 156 86 15 Gertrudis Shorthorn 34 18 1 0 0 0 3418 1 Total 167 156 74 236 238 69 392 385 139

TABLE 19 The GLM Procedure Dependent Variable: STIC Sum of Source DFSquares Mean Square F Value Pr > F Model 297 90.3108958 0.3040771 2.54<.0001 Error 97 11.5922526 0.1195078 Corrected Total 394 101.9031484R-Square Coeff Var Root MSE STIC Mean 0.886242 16.41081 0.3456992.106532 Source DF Type III SS Mean Square F Value Pr > F contemp(Bcode)269 72.05453149 0.26786071 2.24 <.0001 Fingp(Bcode) 3 0.564634730.18821158 1.57 0.2004 Stim 1 0.29426415 0.29426415 2.46 0.1199lox(Bcode) 12 3.23498136 0.26958178 2.26 0.0145 wt 1 0.625475880.62547588 5.23 0.0243 Contrast DF Contrast SS Mean Square F Value Pr >F Additive Test 11-22 1 0.42074192 0.42074192 3.52 0.0636 11-12 12.17031475 2.17031475 18.16 <.0001 12-22 1 0.17567132 0.17567132 1.470.2283 Dominance Test 1 0.97536352 0.97536352 8.16 0.0052 Recessive Test1 0.00964320 0.00964320 0.08 0.7770 OverDominance Test 1 1.587763551.58776355 13.29 0.0004 Standard Parameter Estimate Error t Value Pr >|t| Additive Test 11-22 −0.54698783 0.29151964 −1.88 0.0636 11-12−0.93226650 0.21876446 −4.26 <.0001 12-22 0.38527867 0.31777709 1.210.2283 Dominance Test 1.31754517 0.46119045 2.86 0.0052 Recessive Test0.16170916 0.56927504 0.28 0.7770 OverDominance Test −1.479254330.40583359 −3.64 0.0004 LDL The GLM Procedure Dependent Variable: LD1Sum of Source DF Squares Mean Square F Value Pr > F Model 2954070.356476 13.797819 1.96 <.0001 Error 96 675.059834 7.031873 CorrectedTotal 391 4745.416310 R-Square Coeff Var Root MSE LDl Mean 0.8577456.920726 2.651768 38.31633 Source DF Type III SS Mean Square F ValuePr > F contemp(Bcode) 267 2628.383772 9.844134 1.40 0.0278 Fingp(Bcode)3 25.195055 8.398352 1.19 0.3161 Stim 1 0.195851 0.195851 0.03 0.8678lox(Bcode) 12 160.954945 13.412912 1.91 0.0427 wt 1 5.974613 5.9746130.85 0.3590 Contrast DF Contrast SS Mean Square F Value Pr > F AdditiveTest 1 5.88718974 5.88718974 0.84 0.3625 11 vs 12 1 3.087804453.08780445 0.44 0.5091 12 vs 22 1 1.03256209 1.03256209 0.15 0.7024Dominance Test 1 0.01778689 0.01778689 0.00 0.9600 Recessive Test 13.27515251 3.27515251 0.47 0.4966 OverDominance Test 1 7.236820117.23682011 1.03 0.3129 Standard Parameter Estimate Error t Value Pr >|t| Additive Test 11-22 −2.04608559 2.23617248 −0.91 0.3625 11-12−1.11200497 1.67809812 −0.66 0.5091 12-22 −0.93408062 2.43759636 −0.380.7024 Dominance Test 0.17792435 3.53769859 0.05 0.9600 Recessive Test2.98016622 4.36676923 0.68 0.4966 OverDominance Test −3.158090563.11305081 −1.01 0.3129

TABLE 20 Single Trait Logistic Regression The LOGISTIC Procedure ModelInformation Data Set WORK.EXTRERES Response Variable lox Number ofResponse Levels 3 Number of Observations 389 Link Function LogitOptimization Technique Fisher's scoring Response Profile Ordered TotalValue lox Frequency 1 11 165 2 12 153 3 22  71 NOTE: 8 observations weredeleted due to missing values for the response or explanatory variables.The LOGISTIC Procedure Testing Global Null Hypothesis: BETA = 0 TestChi-Square DF Pr > ChiSq Likelihood Ratio 5.0720 1 0.0243 Score 5.0502 10.0246 Wald 5.0083 1 0.0252 Analysis of Maximum Likelihood EstimatesStandard Parameter DF Estimate Error Chi-Square Pr > ChiSq Intercept 10.9245 0.5608 2.7174 0.0993 Intercept2 1 2.7482 0.5777 22.6278 <.0001sticpred 1 −0.5827 0.2604 5.0083 0.0252 Odds Ratio Estimates Point 95%Wald Effect Estimate Confidence Limits sticpred 0.558 0.335 0.930Multi-trait Logistic Regression The LOGISTIC Procedure Testing GlobalNull Hypothesis: BETA = 0 Test Chi-Square DF Pr > ChiSq Likelihood Ratio14.7234 3 0.0021 Score 14.5859 3 0.0022 Wald 13.9424 3 0.0030 Analysisof Maximum Likelihood Estimates Standard Wald Parameter DF EstimateError Chi-Square Pr > ChiSq Intercept 11 1 −0.9210 0.8702 1.1202 0.2899Intercept 12 1 0.9467 0.8711 1.1813 0.2771 sticpred 1 −0.8681 0.31917.4014 0.0065 stadhpred 1 −0.4025 0.7724 0.2715 0.6023 stpfpred 1 0.56890.1936 8.6313 0.0033 Odds Ratio Estimates Point 95% Wald Effect EstimateConfidence Limits sticpred 0.420 0.225 0.785 stadhpred 0.669 0.147 3.039stpfpred 1.766 1.208 2.581

TABLE 21 The Mixed Procedure Model Information Data Set WORK.CRC1Dependent Variable STIC Covariance Structure Variance ComponentsEstimation Method REML Residual Variance Method Profile Fixed Effects SEMethod Model-Based Degrees of Freedom Method Containment CovarianceParameter Estimates Standard Z Cov Parm Estimate Error Value Pr ZSireID(Bcode) 0.002544 0.002147 1.18 0.1181 contemp(Bcode) 0.0088130.003896 2.26 0.0118 Residual 0.07009 0.005174 13.55 <.0001 Type 3 Testsof Fixed Effects Num Den Effect DF DF F Value Pr > F Fingp(Bcode) 9 31717.50 <.0001 Stim 1 317 1.75 0.1870 lox(Bcode) 8 317 2.36 0.0176 wt 1317 6.45 0.0115 Estimates Standard Label Estimate Error DF t Value Pr >|t| Additive Test 11-22 0.05547 0.07813 317 0.71 0.4782 11-12 0.10440.05660 317 1.84 0.0661 12-22 −0.04890 0.07013 317 −0.70 0.4861Dominance Test −0.1533 0.1007 317 −1.52 0.1289 Recessive Test −0.006570.1373 317 −0.05 0.9619 OverDominance Test 0.1598 0.1170 317 1.37 0.1730Contrasts Num Den Label DF DF F Value Pr > F Additive Test 11-22 1 3170.50 0.4782 11-12 1 317 3.40 0.0661 12-22 1 317 0.49 0.4861 DominanceTest 1 317 2.32 0.1289 Recessive Test 1 317 0.00 0.9619 OverDominanceTest 1 317 1.87 0.1730 Instramuscuar Fat The Mixed Procedure ModelInformation Data Set WORK.CRC1 Dependent Variable Fat CovarianceStructure Variance Components Estimation Method REML Residual VarianceMethod Profile Fixed Effects SE Method Model-Based Degrees of FreedomMethod Containment Covariance Parameter Estimates Standard Z Cov ParmEstimate Error Value Pr Z SireID(Bcode) 0.04848 0.04510 1.07 0.1412contemp(Bcode) 0.3111 0.1060 2.93 0.0017 Residual 1.2479 0.09791 12.75<.0001 Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > FFingp(Bcode) 9 306 7.08 <.0001 Stim 1 306 0.31 0.5790 lox(Bcode) 8 3062.00 0.0461 wt 1 306 74.54 <.0001 Estimates Standard Label EstimateError DF t Value Pr > |t| Additive Test 11-22 0.3765 0.3427 306 1.100.2728 11-12 −0.1207 0.2505 306 −0.48 0.6304 12-22 0.4972 0.3029 3061.64 0.1018 Dominance Test 0.6179 0.4377 306 1.41 0.1591 Recessive Test−0.8737 0.5964 306 −1.47 0.1439 OverDominance Test 0.2558 0.5184 3060.49 0.6220 Contrasts Num Den Label DF DF F Value Pr > F Additive Test11-22 1 306 1.21 0.2728 11-12 1 306 0.23 0.6304 12-22 1 306 2.69 0.1018Dominance Test 1 306 1.99 0.1591 Recessive Test 1 306 2.15 0.1439OverDominance Test 1 306 0.24 0.6220 LDPH The Mixed Procedure ModelInformation Data Set WORK.CRC1 Dependent Variable LDpH CovarianceStructure Variance Components Estimation Method REML Residual VarianceMethod Profile Fixed Effects SE Method Model-Based Degrees of FreedomMethod Containment Covariance Parameter Estimates Standard Z Cov ParmEstimate Error Value Pr Z contemp(Bcode) 0.002771 0.000702 3.95 <.0001Residual 0.007878 0.000572 13.77 <.0001 Type 3 Tests of Fixed EffectsNum Den Effect DF DF F Value Pr > F Fingp(Bcode) 9 363 3.44 0.0004 Stim1 363 1.14 0.2854 lox(Bcode) 8 363 3.01 0.0027 wt 1 363 21.61 <.0001Estimates Standard Label Estimate Error DF t Value Pr > |t| AdditiveTest 11-22 −0.1089 0.02648 363 −4.11 <.0001 11-12 −0.01502 0.01922 363−0.78 0.4351 12-22 −0.09393 0.02376 363 −3.95 <.0001 Dominance Test−0.07891 0.03416 363 −2.31 0.0215 Recessive Test 0.2029 0.04650 363 4.36<.0001 OverDominance Test −0.1240 0.03971 363 −3.12 0.0019 The MixedProcedure Contrasts Num Den Label DF DF F Value Pr > F Additive Test11-22 1 363 16.93 <.0001 11-12 1 363 0.61 0.4351 12-22 1 363 15.63<.0001 Dominance Test 1 363 5.34 0.0215 Recessive Test 1 363 19.04<.0001 OverDominance Test 1 363 9.75 0.0019

TABLE 22 STIC The Mixed Procedure Covariance Parameter EstimatesStandard Z Cov Parm Estimate Error Value Pr Z SireID(Bcode) 0.0061410.004815 1.28 0.1011 contemp(Bcode) 0.05433 0.01003 5.42 <.0001 Residual0.09190 0.006489 14.16 <.0001 Type 3 Tests of Fixed Effects Num DenEffect DF DF F Value Pr > F Fingp(Bcode) 11 369 9.24 <.0001 Stim 2 3694.06 0.0181 lox(Bcode) 12 369 1.90 0.0336 wt 1 369 9.93 0.0018 EstimatesStandard Label Estimate Error DF t Value Pr > |t| Additive Test 11-220.06327 0.08142 369 0.78 0.4376 11-12 0.07985 0.05752 369 1.39 0.165912-22 −0.01658 0.07440 369 −0.22 0.8238 Dominance Test −0.09643 0.1052369 −0.92 0.3598 Recessive Test −0.04669 0.1450 369 −0.32 0.7476OverDominance Test 0.1431 0.1198 369 1.20 0.2328 Contrasts Num Den LabelDF DF F Value Pr > F Additive Test 11-22 1 369 0.60 0.4376 11-12 1 3691.93 0.1659 12-22 1 369 0.05 0.8238 Dominance Test 1 369 0.84 0.3598Recessive Test 1 369 0.10 0.7476 OverDominance Test 1 369 1.43 0.2328STL Covariance Parameter Estimates Standard Z Cov Parm Estimate ErrorValue Pr Z SireID(Bcode) 0.4389 0.3429 1.28 0.1003 contemp(Bcode) 3.36080.7375 4.56 <.0001 Residual 10.6090 0.6789 15.63 <.0001 Type 3 Tests ofFixed Effects Num Den Effect DF DF F Value Pr > F Fingp(Bcode) 11 36816.40 <.0001 Stim 2 368 4.68 0.0098 lox(Bcode) 12 368 2.18 0.0124 wt 1368 8.64 0.0035 Estimates Standard Label Estimate Error DF t Value Pr >|t| Additive Test 11-22 −0.7500 0.8582 368 −0.87 0.3827 11-12 −0.67320.5998 368 −1.12 0.2625 12-22 −0.07683 0.7815 368 −0.10 0.9217 DominanceTest 0.5963 1.0975 368 0.54 0.5872 Recessive Test 0.8268 1.5279 368 0.540.5887 OverDominance Test −1.4232 1.2576 368 −1.13 0.2585 Contrasts NumDen Label DF DF F Value Pr > F Additive Test 11-22 1 368 0.76 0.382711-12 1 368 1.26 0.2625 12-22 1 368 0.01 0.9217 Dominance Test 1 3680.30 0.5872 Recessive Test 1 368 0.29 0.5887 OverDominance Test 1 3681.28 0.2585

TABLE 23 The Mixed Procedure Covariance Parameter Estimates Standard ZCov Parm Estimate Error Value Pr Z SireID(Bcode) 0.005452 0.004008 1.360.0869 contemp(Bcode) 0.03915 0.008774 4.46 <.0001 Residual 0.091050.006426 14.17 <.0001 Type 3 Tests of Fixed Effects Num Den Effect DF DFF Value Pr > F Fingp(Bcode) 10 368 10.46 <.0001 Stim 2 368 5.89 0.0030lox(Bcode) 10 368 2.28 0.0132 wt 1 368 7.14 0.0079 Estimates StandardLabel Estimate Error DF t Value Pr > |t| Additive Test 11-22 0.062630.07965 368 0.79 0.4322 11-12 0.09181 0.05618 368 1.63 0.1031 12-22−0.02918 0.07277 368 −0.40 0.6887 Dominance Test −0.1210 0.1028 368−1.18 0.2398 Recessive Test −0.03345 0.1419 368 −0.24 0.8137OverDominance Test 0.1544 0.1171 368 1.32 0.1879 Contrasts Num Den LabelDF DF F Value Pr > F Additive Test 11-22 1 368 0.62 0.4322 11-12 1 3682.67 0.1031 12-22 1 368 0.16 0.6887 Dominance Test 1 368 1.39 0.2398Recessive Test 1 368 0.06 0.8137 OverDominance Test 1 368 1.74 0.1879

REFERENCES

The contents of the following documents are incorporated herein byreference:

Andersson-Eklund, L. and Rendel, J. 1993. Linkage between amylase-Ilocus and a major gene for milk fat content in cattle. Animal Genetics24, 101-103.

Barendse, W.1997. Assessing lipid metabolism. Patent applicationPCT/AU98/00882

W. Barendse and B. Harrison (2001) The analysis of effects on instroncompression and adhesion in the semitendinosus muscle of genotypes atthe candidate gene Lysyl Oxidase (LOX) in cattle of diverse breeds. CRCcommercial-in-confidence report.

Barendse, W., Harrison, B. and Li, Y. 2000. The analysis of effects onpeak-force of genotypes at the candidate gene Calpastatin in cattle ofdiverse breeds. Confidential Report of the Beef Quality CRC.

Chung, H. Y., Davis, M. E. and Hines, H. C. 1999. A DNA polymorphism ofthe bovine calpastatin gene detected by SSCP analysis. Animal Genetics30, 80.

Chung, H. Y., Davis, M. E., Hines, H. C. and Wulf, D. M. 1999.Relationship of a PCR-SSSCP at the bovine Calpastatin locus withCalpastatin activity and. Meat Tenderness. Ohioline Bulletin SpecialCircular 170-99.http://www.ag.ohio-state.edu/˜ohioline/sc170/sc170_(—)3.html

Coleman, J. B., Cucca, F., Hearne, C. M., Cornall, R. J., Reed, P. W.,Ronningen, K. S., Undlien, D. E., Nistico, L., Buzzetti, R., Tosi, R.,Pociot, F., Nerup, J., ornelis, F., Barnett, A. H., Bain, .C., and Todd,J. A. 1995. Linkage disequilibrium mapping of a type 1 diabetessusceptibility gene (IDDM7) to chromosome 2q31-q33. Nature Genetics 9,80-85.

Cronlund, A. L., Smith, B. D., Kagan, H. M. 1985. Binding of lysyloxidase to fibrils of type I Collagen. Connective Tissue Research 14,109-119.

Drinkwater, R. D., Harrison, B., Byrne, K., Botero, F. A., Knight, M.,Davis, G. P., Lenane, I., Li, Y., Kuipers, R., and Moore, S. S. 1999.Candidate genes for Meat Quality, draft report for the 1998-1999research program. Cattle and Beef CRC Commercial-In-Confidence Report.

Ekholm, E. C., Ravanti, L., Kahari, V., Paavolainen, P. and Penttinen,R. P. 2000. Expression of extracellular matrix genes; transforminggrowth factor (TGF)-betal and ras in tibial fracture healing oflathyritic rats. Bone 27, 551-557.

Geesink, G. H. and Koohmaraie, M. 1999. Postmortem proteolysis andcalpain/calpastatin activity in callipyge and normal lamb biceps femorisduring extended postmortem storage. J. Anim. Sci. 77, 1490-1501.

Giampuzzi, M., Botti, G., Cilli, M., Gusmano, R., Borel, A., Sommer, P.,Di Donato, A. 2001. Down regulation of lysyl oxidase induced tumorigenictransformation in NRK-49F cells characterized by constitutive activationof Ras proto-oncogene. Journal of Biological Chemistry [epub ahead ofprint].

J.Henshall and M. Goddard (1999) Multiple-trait mapping of quantitativetrait loci after selective genotyping using logistic regression.Genetics 151:885-894.

Y. Li (2000) CRC Molecular Genetics Program Annual Report. CRCcommercial-in-confidence report.

Lonergan, S. M., Ernst, C. W., Bishop, M. D., Calkins, C. R., andKoohmaraie, M. 1995. Relationship of restriction fragment lengthpolymorphisms (RFLP) at the bovine calpastatin locus to calpastatinactivity and meat tenderness. J.Anim. Sci 73, 3608-3612.

Koohmaraie, M. 1994. Muscle proteinases and meat aging meat Sci. 36:93.

Nellaiappan, K., Risitano, A., Liu, G., Nicklas, G. and Kagan, K. M.2000. Fully processed lysyl oxidase catalyst translocates from theextracellular space into nuclei of aortic smooth-muscle cells. Journalof Cell Biochemistry 79, 576-582.

Nonneman, D., Kappes, S. M. and Koohmaraie, M. 1999. Rapidcommunication: a polymorphic microsatellite in the promotor region ofthe bovine calpastatin gene. J. Anim. Sci 77, 3114-3115.

Slee, R. B., Hillier, S. G., Largue, P., Harlow, C. R., Miele, G. andClinton, M. 2001. Differentiation-dependent expression of connectivetissue growth factor and lysyl oxidase messenger ribonucleic acids inrat granulosa cells. Endocrinology 142, 1082-1089.

Terwilliger, J. D. 1995. A powerful likelihood method for the analysisof linkage disequilibrium between trait loci and one or more polymorphicmarker loci. American Journal of Human Genetics 56, 777--787.

Whipple, G., Koohmaraie, M., Dikeman, M. E., Crouse, J. D., Hunt, M. C.,Klemm, R. D. 1990. Evaluation of attributes that affect longissimusmuscle tenderness in Bos taurus and Bos indicus cattle. J.Anim. Sci. 68,2716-2728.

Woodward, B. W., DeNise, S. K., and Marchello, J. A. 2000. Evaluation ofcalpastatin activity measures in ante-and postmortem muscle fromhalf-sib bulls and steers. J.Anim. Sci. 78, 804-809.

1. A method for assessing the tenderness of meat from an animal,comprising the step of testing the animal for the presence of CAST3 D/Eallele 1, 2 or 3, wherein the animal is bovine cattle, wherein thepresence of CAST3 D/E allele 2 or 3 indicative of greater meattenderness in comparison to the presence of CAST3 D/E allele 1, andwherein said CAST3 D/E alleles 1, 2 and 3 have the nucleotide sequencesof SEQ ID NOS:1, 2 and 3, respectively.
 2. A method for selecting ananimal likely to yield meat of improved tenderness, comprising the stepsof: (1) testing the animal for the presence of CAST3 D/E allele 1, 2 and3 ; and (2) selecting animals which have the CAST3 D/E allele 2 or 3 ,wherein the animals are bovine cattle, and wherein said CAST3 D/Ealleles 1, 2 and 3 have the nucleotide sequences of SEQ ID NOS:1, 2 and3, respectively.
 3. A method as claimed in claim 2 wherein the alleletested for is said CAST3 D/E allele 2 and cattle that are homozygous forthis allele are selected.
 4. A method as claimed in claim 3 furthercomprising the step of testing for the additional presence of one ormore alleles selected from the group consisting of the CAST3 D/E allele1 and CAST3 D/E allele
 3. 5. A method as claimed in claim 1 furthercomprising breeding the selected animal.
 6. A method for assessing thetenderness of meat from an animal, comprising the step of testing theanimal for the presence of CAST3 D/E allele 1, 2 or 3, wherein theanimal is bovine cattle selected from the group consisting of Herefordand Angus, wherein the presence of CAST3 D/E allele 2 or 3 is indicativeof greater meat tenderness in comparison to the presence of CAST3 D/Eallele 1, and wherein said CAST3 D/E alleles 1, 2 and 3 have thenucleotide sequences of SEQ ID NOS:1, 2 and 3, respectively.
 7. A methodfor selecting an animal likely to yield meat of improved tenderness,comprising the steps of: (1) testing the animal for the presence ofCAST3 D/E allele 1, 2 or 3 ; and (2) selecting animals which have theCAST3 D/E allele 2 or 3, wherein the animals are bovine cattle selectedfrom the group consisting of Hereford and Angus, and wherein said CAST3D/E alleles 1, 2 and 3 have the nucleotide sequences of SEQ ID NOS:1, 2and 3, respectively.
 8. A method as claimed in claim 7 wherein theallele tested for is said CAST3 D/E allele 2 and bovine cattle that arehomozygous for this allele are selected.
 9. A method as claimed in claim8 further comprising the step of testing for the additional presence ofone or more alleles selected from the group consisting of the CAST3 D/Eallele 1 and CAST3 D/E allele
 3. 10. A method as claimed in claim 7further comprising breeding the selected animal.